Use the PlayerBBall.csv dataset to visually represent (summarize) the number of players in each position.

Below bar chart shows the count in each category of position for the basketball player dataset.

Use the dataset to visually investigate the distribution of the weight of centers (C) is greater than the distribution of the weight of forwards (F)

As shown in the Geom Point bar below, the distribution of the weight of centers (C) is greater than the distribution of the weight of forwards (F).

Use the dataset to visually investigate if the distribution of the height of centers (C) is greater than the distribution of the height of forwards (F).

As shown in the Geom Point bar below, the distribution of the height of centers (C) is greater than the distribution of the height of forwards (F).

Use the dataset to visually investigate if the distribution of height is different between any of the positions.

As shown in the geom point bar below, distribution of height is different for different positions. However, it is almost similar for category F-G and G-F. And for C-F and F-C. We are keeping the tallest as Centers and the shortest as Guards.

A historian would like to investigate the claim that the heights of players have increased over the years. Analyze this claim graphically / visually.

It does not look like Player’s height significantly increased over the years. The below graph shows it almost stayed constant.

Create a 3D plot of height vs. weight vs. year and color code the points by position.

Go to this website and use one of the 50 best plots to visualize some aspect of the data and provide at least one insight. http://r-statistics.co/Top50-Ggplot2-Visualizations-MasterList-R-Code.html

I am using box plot to find out the relationship between Player’s position and height in Inches.

Visually test the claim that the distribution of incomes increase (mean or median) as the education level rises.

This concludes Unit-2 assignment.